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Book Logical and Relational Learning (Cognitive Technologies)

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Logical and Relational Learning (Cognitive Technologies)

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    Available in PDF - DJVU Format | Logical and Relational Learning (Cognitive Technologies).pdf | Language: ENGLISH
    Luc De Raedt(Author)

    Book details


This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.

From the reviews: This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic. The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.

4.5 (8947)
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Book details

  • PDF | 387 pages
  • Luc De Raedt(Author)
  • Springer; Softcover reprint of hardcover 1st ed. 2008 edition (November 23, 2010)
  • English
  • 5
  • Computers & Technology

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Review Text

  • By Yakov Zaytsev on February 8, 2014

    I would not share the enthusiasm of previous reviewersDe Raedt's book is indeed helpful to get the terminology and pointers in case one is only beginning diving into relational learningThe bibliography is excellentThe material is confusing at times. That's because of sparse and somewhat trivial examples at placesPersonally it was hard to follow the detailsThe book does good job if reader wants to grasp the big picture (though it is heavily biased towards author research interests)On a side note I'd like to recommend an out of print book by Lavrac and Dzeroski "Inductive Logic Programming" which excels at providing both empirical(learning usually single concept from examples) and interactive perspective as well as detailed explanation of inner working of several systemsThe later is strong point and De Raedt's book lacks exactly thisMost of the time ILP implementations are bags of techniquesAs practitioner get the Lavrac and Dzeroski book first and keep De Raedt's for light overview of slightly biased latests trendsConcerning fundamentals there are other books which do better job

  • By Ni Lao on May 28, 2012

    I am working on my PhD thesis in statistical relational learning. I've spent quite some time looking for a good book in relational learning, and found this one much better than any other alternatives. It has comprehensive explanations to ILP concepts with detailed examples, which are very important to me.

  • By King Yin Yan on May 25, 2010

    This is the best book on this topic thus far, by a long-time practitioner in the field. In particular it covers theory revision which is a very challenging area.One can see the field has matured when the theory is described in a very elegant and abstract manner. The book is very well organized. If you're into logic-based learning you must get this book!


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